Fuzzy Logic Based Regression Models for Electronics Manufacturing Applications

In recent years, much attention has been focused on the ability of fuzzy logic to solve problems that were previously assumed difficult to solve. These include, for example, the use of fuzzy logic for control and pattern recognition. However, the application of fuzzy logic to the establishment of correlations between independent and dependent process variables, i.e. regression modeling, has received less attention. Fuzzy logic based regression models have proven capable of accurately representing a wide variety of processes, from the type usually modeled with traditional methods such as polynomial regression, to very complex multi-dimensional relationships often modeled with neural networks. The fuzzy models can be used for a variety of tasks such as gaining an increased understanding of the process itself or determining the input settings for optimum process operation. Often, the nature of the fuzzy model results in some saving or benefit when compared with other modeling techniques. In this paper, we will describe a fuzzy logic regression modeling technique (often termed “TSK fuzzy reasoning”). We will then detail some particular electronics manufacturing applications for which the fuzzy logic based regression models have proven useful. These applications include the optimization of a simulated vertical CVD process and the use of specialized fuzzy logic based regression models for ranking process effects (ANOVA).